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IC-Service: A Service-Oriented Approach to the Development of Recommendation Systems. Aliaksandr Birukou, Enrico Blanzieri, Vincenzo D'Andrea, Paolo Giorgini, Natallia Kokash, Alessio Modena. Introduction. Recommendation systems Service-Oriented Computing Implicit Culture

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IC-Service: A Service-Oriented Approach to the Development of Recommendation Systems


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    1. IC-Service: A Service-Oriented Approach to the Development of Recommendation Systems Aliaksandr Birukou, Enrico Blanzieri, VincenzoD'Andrea, Paolo Giorgini, Natallia Kokash, Alessio Modena ACM SAC, Seoul, Korea

    2. Introduction • Recommendation systems • Service-Oriented Computing • Implicit Culture • System for Implicit Culture Support (SICS) • SICS Architecture • Main modules • Configuration • Applications • Web service discovery • Conclusions • References ACM SAC, Seoul, Korea

    3. Recommendation systems • Prune large information spaces in searching for items of interest • Examples • movies (MovieLens), • music (JUKE-BOX), • books (Amazon), • hotels (TripAdvisor) • … • Meta-recommendation systems • Work with data from multiple (heterogeneous) information sources • MetaLens [Schafer et al., 2002] ACM SAC, Seoul, Korea

    4. Service Registry Publish Bind Find Service Client Service Provider Service-oriented computing Web service description • Requirements for a recommendation service: • Use in various application domains • Ability to store heterogeneous client data • Adaptability to the needs of a particular client • Ability to process data according to the domain specific rules Service-oriented application Web service ACM SAC, Seoul, Korea

    5. Implicit Culture (IC): motivation and goals • Communities of human/artificial agents have knowledge specific to their activities, i.e., community culture • The knowledge is often implicit and highly personalized • Encourage a newcomer to behave according to a community culture • Transfer knowledge implicitly (without special efforts for its analysis and description) • http://www.dit.unitn.it/~implicit • [Blanzieri et al., 2001] ACM SAC, Seoul, Korea

    6. Extract actions performed in different situations Suggest actions in a given situation Observe agents’ actions IC definitions • Action – something that can be done • Agent (actor) – somebody or something performing an action • Object – something that passively participate in the action • Situation – a state of the world faced by the agent. Includes a set of objects and a set of possible actions • Culture – a usual behavior of the group of agents • Group G – group of agents which behaviour is observed • Group G'– group of agents who require recommendations • Implicit Culture relation – situations in which agents of the group Gbehave similarly to agents of the group G' • System for Implicit Culture Support (SICS) – a system which tries to establish IC relation ACM SAC, Seoul, Korea

    7. System for Implicit Culture Support (SICS) Produce a theory about common user behavior Produce recommendation about action Stores information about actions ACM SAC, Seoul, Korea

    8. SICS Architecture • SICS Core • SICS layer infers theory rules and recommends actions • Configuration and storage layer manages theory • SICS Remote Module defines protocols for information exchange with the client • SICS Remote Client provides a simple interface for remote clients ACM SAC, Seoul, Korea

    9. Storage Module • Observations • Agents (1…N), • Actions (1), • Objects (0…N), • Attributes (0…N) • Scenes (1…N) • no agents • no timestamps • Theory rules • if consequent (predicates) then antecedent (predicates) • Predicates: • Conditions on observations (action- predicates) • Conditions on time (temporal-predicates) ACM SAC, Seoul, Korea

    10. Inductive Module • Analyses observations and generates theory rules for an actor or a group of actors • “Apriori” algorithm for mining association rules [Agrawal & Srikant, 1994] • A transaction is a sequence of executed actions A1,…,AN (can be obtained from observations using timestamps) • An association rule is an implicationof the form A1 A2 where A1, A2 are actions, A1 A2 • The rule holds with confidencec if c% of transactions that contain A1 also contain A2 • The rule A1 A2 has support s in the transaction set s% of transactions contain A1 A2 • Generate association rules that have support and confidence greater than predefined minimum support and minimum confidence. ACM SAC, Seoul, Korea

    11. Composer Module • Cultural Action Finder (CAF) • Matches actions executed by agents from group G’ with antecedents of the theory rules • Matching algorithms • Returns consequences of the theory rules (cultural actions) • Scene producer • Finds a set of agents that have performed actions similar to a cultural action for the agent X • Selects a set of agents similar to an agent X and a set of scenes S in which they have performed the actions • Select and propose to X a scene from S ACM SAC, Seoul, Korea

    12. Instance Configuration • Composer constants: • Similarity threshold • Number of nearest neighbors • Return all scenes or only the best • Max number of observations • Names of groups G and G’ • Configuration of similarity functions: • Rules for calculating similarity among observations • Similarity weights for elements (names and values) • exceptions, instants and default • Case sensitive or not • Regular expressions • Inductive Module constants ACM SAC, Seoul, Korea

    13. Applications • Prototypes: • Recommending Web links [Birukou et al., 2005] • Recommending scientific publications • Quality-based Indexing of Web Information (QUIEW) http://quiew.itc.it/ • Supporting Polymerase Chain Reaction (PCR) experiments [Mullis et al., 1986] [Sarini et al., 2004] • Software patterns selection • Web service discovery ACM SAC, Seoul, Korea

    14. Web Service (WS) discovery • Meeting functionality required by a user with specifications of existing web services • Problems: incomplete specifications, broken links, unfair providers… • Choosing a service with good quality characteristics • Problems: often QoS data are not available, some of them are context-dependent… • Implicit Culture approach • Analyze which web services have been previously used for similar problems by clients with similar interests • Use up-to-date information to improve service discovery and QoS-driven selection ACM SAC, Seoul, Korea

    15. A system for WS discovery Search process Monitoring process ACM SAC, Seoul, Korea

    16. WS discovery in terms of IC • Observations • Actors • Applications (application name, user name, location) • Users (user name, location) • Objects • Operations (operation name, web service name) • Inputs/Outputs (parameter name, parameter value) • Requests (goals, operations, inputs/outputs) • Actions • Invoke (timestamp, operation, input) • Get response (timestamp, operation, output, response time) • Raise exception (timestamp, operation, exception type, input) • Provide feedback (timestamp, QoS parameters) • Submit request (timestamp, request) • Rules • if submit request(request) then invoke(operation-X(service-Y), request). • Similarity measures: • Vector Space Model (VSM) • Term Frequency- Inverse Document Frequency (TF-IDF) metric • WordNet-based semantic similarity measure ACM SAC, Seoul, Korea

    17. VSM WordNet A system for WS discovery: experimental results • 20 web services (http://www.xMethods.com) divided into 5 categories [Kokash et al., 2007] • 4 clients submit 100 requests ACM SAC, Seoul, Korea

    18. Conclusions • Ubiquity • The IC-service can be accessed from any workplace • Reusability • A unique solution for various distributed communities • Integration • The knowledge transfer between communities is facilitated • Scalability • 100000 observations of 100 users for one instance • Composition of several IC-Services is possible • Portability • XML storage • Customization • Ability of runtime configuring of theory rules… ACM SAC, Seoul, Korea

    19. References • [Schafer et al., 2002] J. B. Schafer, J. A. Konstan, and J. Riedl. Meta-recommendation systems: user-controlled integration of diverse recommendations. In Proc. of the Int. Conference on Information and Knowledge Management, pages 43-51. ACM Press, 2002. • [Blanzieri et al., 2001] E. Blanzieri, P. Giorgini, P. Massa, and S. Recla. Implicit culture for multi-agent interaction support. In CooplS: Proc. of the 9th Int. Conference on Cooperative Information Systems, volume 2172 of LNCS, pages 27-39. Springer, 2001. • [Birukov et al., 2005] A. Birukov, E. Blanzieri, and P. Giorgini. Implicit: An agent-based recommendation system for web search. In AAMAS: Proc. of the 4th Int. Joint Conference on Autonomous Agents and Multiagent Systems, pages 618-624. ACM Press, 2005. • [Mullis et al., 1986] K. B. Mullis, F. A. Faloona, S. Scharf, R. K. Saiki, G. Horn, H. A. Erlich. Specific enzymatic amplification of DNA in vitro: the polymerase chain reaction. In Cold Spring Harbor Symposia on Quantitative Biology, volume 51, pages 263-273, 1986. • [Sarini et al., 2004] M. Sarini, E. Blanzieri, P. Giorgini, C. Moser. From actions to suggestions: supporting the work of biologists through laboratory notebooks. In COOP: Proc. of 6th Int. Conference on the Design of Cooperative Systems, pages 131-146. IOS Press, 2004. • [Agrawal & Srikant, 1994] R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In VLDB: Proc. of the 20th Int. Conference on Very Large Data Bases, pages 487-499. Morgan Kaufmann, 1994. • [Kokash et al., 2007] N. Kokash, A. Birukou, V. D'Andrea: Web service discovery based on past user experience. In: International Conference on Business Information Systems (BIS), to appear, Springer (2007) ACM SAC, Seoul, Korea